{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "98da37c7-87ba-4008-a670-38d13fc494fd",
   "metadata": {},
   "source": [
    "## Isolation Forest Anomaly Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "50e89513-a3aa-43df-b84b-3691e4a1588f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.616671</td>\n",
       "      <td>1.944522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.256461</td>\n",
       "      <td>1.609444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.343919</td>\n",
       "      <td>4.392961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.195393</td>\n",
       "      <td>2.794485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-3.329586</td>\n",
       "      <td>5.303160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1\n",
       "0  1.616671  1.944522\n",
       "1  1.256461  1.609444\n",
       "2 -2.343919  4.392961\n",
       "3  1.195393  2.794485\n",
       "4 -3.329586  5.303160"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv('healthcare.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6061c71b-310c-4dd7-bce2-3bfdca55bba2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f5b9b43ea10>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(df.iloc[:,0], df.iloc[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "124a970f-bcfe-4081-95ed-3c37d094b76a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import IsolationForest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bb807655-f596-4796-97ec-a6430ff1f7d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/sklearn/base.py:409: UserWarning: X does not have valid feature names, but IsolationForest was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "clf = IsolationForest(contamination=0.2)\n",
    "clf.fit(df)\n",
    "predictions = clf.predict(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3162132d-8be5-4299-8e76-35d2fd9d86d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1, -1,  1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1, -1,  1,  1, -1, -1,  1,\n",
       "        1,  1, -1,  1, -1,  1,  1,  1,  1,  1, -1, -1, -1,  1,  1,  1, -1,\n",
       "        1,  1,  1,  1, -1,  1, -1,  1,  1,  1, -1,  1,  1,  1,  1, -1,  1,\n",
       "       -1,  1, -1,  1,  1,  1,  1, -1,  1,  1,  1,  1, -1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,\n",
       "       -1,  1,  1,  1,  1,  1,  1, -1,  1,  1,  1, -1,  1, -1, -1,  1,  1,\n",
       "        1,  1,  1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,  1,  1, -1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,  1, -1, -1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1, -1,  1, -1,  1,  1, -1,  1,  1,  1,  1,\n",
       "       -1,  1,  1, -1,  1,  1,  1,  1,  1,  1, -1,  1, -1,  1,  1,  1,  1,\n",
       "       -1,  1,  1,  1,  1, -1,  1,  1, -1,  1, -1,  1,  1, -1,  1,  1, -1,\n",
       "       -1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1, -1,  1,  1,  1,  1, -1, -1,  1])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6e8b9bfb-1ef3-463c-863b-741ec083bb07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  4,  20,  24,  45,  48,  49,  53,  55,  61,  62,  63,  67,  72,\n",
       "         74,  78,  83,  85,  87,  92,  97, 108, 114, 119, 126, 130, 132,\n",
       "        133, 141, 151, 160, 166, 167, 177, 179, 182, 187, 190, 197, 199,\n",
       "        204, 209, 212, 214, 217, 220, 221, 227, 242, 247, 248]),)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "index = np.where(predictions < 0)\n",
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "176d346c-e2f1-493b-acb8-758345d07ec9",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "de2c580e-6c53-411f-98ee-a3d6e6d4f388",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f5b8ee40ac0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "index = np.where(predictions < 0)\n",
    "plt.scatter(df.iloc[:,0], df.iloc[:,1])\n",
    "plt.scatter(x[index,0], x[index,1], edgecolors=\"r\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0cc6b328-e0ec-4b39-b8d6-27b496bb4c6d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62fdf5a7-3e96-464e-b598-a776f2e0932f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8811162-9e47-45fe-bda3-d92caebd0748",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19cf67aa-e543-472d-ade6-54041860854d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e458313-8e01-48ca-8dd4-9ce0d01cad5a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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